Report details

print(paste0("Experiment name: ", experiment_name))
## [1] "Experiment name: Experiment 1 (1 motif, 300 sequences, 50\\% positive sequences)"

Time

plot_times(total_times)

ggsave(paste0("plots/", exp_prefix, "_times_figure.pdf"))
Saving 7 x 5 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.6]{sections/plots/", exp_prefix,"_times_figure.pdf}\n",
    "\\caption{", experiment_name, " - box plots of computation times for each k-mer filtering method.}\n",
    "\\label{fig:", exp_prefix,"_times_figure}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.6]{sections/plots/exp1_1m_300s_50p_times_figure.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - box plots of computation times for each k-mer filtering method.}
\label{fig:exp1_1m_300s_50p_times_figure}
\end{figure}
knitr::kable(table_times(total_times))
Time
QuiPT 27.52 +- 2.04
Chi-squared 333.82 +- 10.36
SU 9.9 +- 0.27
IG 9.95 +- 0.32
GR 9.97 +- 0.25
NJMIM 1815.35 +- 35.34
MRMR 1392.71 +- 28.86
JMIM 1696.74 +- 37.5
JMI 2571.82 +- 125.23
DISR 2791.62 +- 241.4
FCBF 67.47 +- 2.16
xtable(table_times(total_times),
       label = paste0(exp_prefix, "_times_table"),
       caption = paste0(experiment_name, " - average computation time for each filtering method."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun  6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rl}
  \hline
 & Time \\ 
  \hline
QuiPT & 27.52 +- 2.04 \\ 
  Chi-squared & 333.82 +- 10.36 \\ 
  SU & 9.9 +- 0.27 \\ 
  IG & 9.95 +- 0.32 \\ 
  GR & 9.97 +- 0.25 \\ 
  NJMIM & 1815.35 +- 35.34 \\ 
  MRMR & 1392.71 +- 28.86 \\ 
  JMIM & 1696.74 +- 37.5 \\ 
  JMI & 2571.82 +- 125.23 \\ 
  DISR & 2791.62 +- 241.4 \\ 
  FCBF & 67.47 +- 2.16 \\ 
   \hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - average computation time for each filtering method.} 
\label{exp1_1m_300s_50p_times_table}
\end{table}

Ranking results

model = "LR LASSO"
metrics = c("Accuracy", "AUC")
knitr::kable(table_nonranking(nonranking_results, model, metrics))
n-kmers selected Accuracy AUC
FCBF 9.8 +- 0.841 0.977 +- 0.006 0.969 +- 0.017
QuiPT 0.01 49.9 +- 9.273 0.93 +- 0.006 0.875 +- 0.032
QuiPT 0.05 75.4 +- 12.058 0.932 +- 0.006 0.9 +- 0.028
Chi-squared 0.01 573.3 +- 60.234 0.927 +- 0.005 0.899 +- 0.016
Chi-squared 0.05 812.9 +- 71.2 0.924 +- 0.005 0.887 +- 0.016
xtable(table_nonranking(nonranking_results, model, metrics),
       label = paste0(exp_prefix, "_nonranking_LR_table"),
       caption = paste0(experiment_name, " - averaged filtering results for LASSO classifier."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun  6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
  \hline
 & n-kmers selected & Accuracy & AUC \\ 
  \hline
FCBF & 9.8 +- 0.841 & 0.977 +- 0.006 & 0.969 +- 0.017 \\ 
  QuiPT 0.01 & 49.9 +- 9.273 & 0.93 +- 0.006 & 0.875 +- 0.032 \\ 
  QuiPT 0.05 & 75.4 +- 12.058 & 0.932 +- 0.006 & 0.9 +- 0.028 \\ 
  Chi-squared 0.01 & 573.3 +- 60.234 & 0.927 +- 0.005 & 0.899 +- 0.016 \\ 
  Chi-squared 0.05 & 812.9 +- 71.2 & 0.924 +- 0.005 & 0.887 +- 0.016 \\ 
   \hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for LASSO classifier.} 
\label{exp1_1m_300s_50p_nonranking_LR_table}
\end{table}
model = "1-NN"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
n-kmers selected Accuracy AUC
FCBF 9.8 +- 0.841 0.943 +- 0.014 0.893 +- 0.018
QuiPT 0.01 49.9 +- 9.273 0.859 +- 0.015 0.599 +- 0.017
QuiPT 0.05 75.4 +- 12.058 0.882 +- 0.01 0.607 +- 0.018
Chi-squared 0.01 573.3 +- 60.234 0.895 +- 0.006 0.558 +- 0.013
Chi-squared 0.05 812.9 +- 71.2 0.89 +- 0.007 0.527 +- 0.006
xtable(table_nonranking(nonranking_results, model, metrics),
       label = paste0(exp_prefix, "_nonranking_1NN_table"),
       caption = paste0(experiment_name, " - averaged filtering results for 1-nearest neighbor classifier."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun  6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
  \hline
 & n-kmers selected & Accuracy & AUC \\ 
  \hline
FCBF & 9.8 +- 0.841 & 0.943 +- 0.014 & 0.893 +- 0.018 \\ 
  QuiPT 0.01 & 49.9 +- 9.273 & 0.859 +- 0.015 & 0.599 +- 0.017 \\ 
  QuiPT 0.05 & 75.4 +- 12.058 & 0.882 +- 0.01 & 0.607 +- 0.018 \\ 
  Chi-squared 0.01 & 573.3 +- 60.234 & 0.895 +- 0.006 & 0.558 +- 0.013 \\ 
  Chi-squared 0.05 & 812.9 +- 71.2 & 0.89 +- 0.007 & 0.527 +- 0.006 \\ 
   \hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for 1-nearest neighbor classifier.} 
\label{exp1_1m_300s_50p_nonranking_1NN_table}
\end{table}
model = "16-NN"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
n-kmers selected Accuracy AUC
FCBF 9.8 +- 0.841 0.912 +- 0.002 0.954 +- 0.016
QuiPT 0.01 49.9 +- 9.273 0.9 +- 0 0.759 +- 0.034
QuiPT 0.05 75.4 +- 12.058 0.9 +- 0 0.769 +- 0.039
Chi-squared 0.01 573.3 +- 60.234 0.9 +- 0 0.636 +- 0.025
Chi-squared 0.05 812.9 +- 71.2 0.9 +- 0 0.571 +- 0.016
xtable(table_nonranking(nonranking_results, model, metrics),
       label = paste0(exp_prefix, "_nonranking_16NN_table"),
       caption = paste0(experiment_name, " - averaged filtering results for 16-nearest neighbor classifier."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun  6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
  \hline
 & n-kmers selected & Accuracy & AUC \\ 
  \hline
FCBF & 9.8 +- 0.841 & 0.912 +- 0.002 & 0.954 +- 0.016 \\ 
  QuiPT 0.01 & 49.9 +- 9.273 & 0.9 +- 0 & 0.759 +- 0.034 \\ 
  QuiPT 0.05 & 75.4 +- 12.058 & 0.9 +- 0 & 0.769 +- 0.039 \\ 
  Chi-squared 0.01 & 573.3 +- 60.234 & 0.9 +- 0 & 0.636 +- 0.025 \\ 
  Chi-squared 0.05 & 812.9 +- 71.2 & 0.9 +- 0 & 0.571 +- 0.016 \\ 
   \hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for 16-nearest neighbor classifier.} 
\label{exp1_1m_300s_50p_nonranking_16NN_table}
\end{table}
model = "RF (500 trees)"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
n-kmers selected Accuracy AUC
FCBF 9.8 +- 0.841 0.968 +- 0.006 0.971 +- 0.016
QuiPT 0.01 49.9 +- 9.273 0.912 +- 0.004 0.855 +- 0.038
QuiPT 0.05 75.4 +- 12.058 0.913 +- 0.003 0.877 +- 0.035
Chi-squared 0.01 573.3 +- 60.234 0.908 +- 0.003 0.908 +- 0.018
Chi-squared 0.05 812.9 +- 71.2 0.905 +- 0.003 0.891 +- 0.024
xtable(table_nonranking(nonranking_results, model, metrics),
       label = paste0(exp_prefix, "_nonranking_RF500_table"),
       caption = paste0(experiment_name, " - averaged filtering results for random forest classifier (500 trees)."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun  6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
  \hline
 & n-kmers selected & Accuracy & AUC \\ 
  \hline
FCBF & 9.8 +- 0.841 & 0.968 +- 0.006 & 0.971 +- 0.016 \\ 
  QuiPT 0.01 & 49.9 +- 9.273 & 0.912 +- 0.004 & 0.855 +- 0.038 \\ 
  QuiPT 0.05 & 75.4 +- 12.058 & 0.913 +- 0.003 & 0.877 +- 0.035 \\ 
  Chi-squared 0.01 & 573.3 +- 60.234 & 0.908 +- 0.003 & 0.908 +- 0.018 \\ 
  Chi-squared 0.05 & 812.9 +- 71.2 & 0.905 +- 0.003 & 0.891 +- 0.024 \\ 
   \hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for random forest classifier (500 trees).} 
\label{exp1_1m_300s_50p_nonranking_RF500_table}
\end{table}
model = "RF (1000 trees)"
knitr::kable(table_nonranking(nonranking_results, model, metrics))
n-kmers selected Accuracy AUC
FCBF 9.8 +- 0.841 0.968 +- 0.006 0.971 +- 0.016
QuiPT 0.01 49.9 +- 9.273 0.912 +- 0.005 0.857 +- 0.037
QuiPT 0.05 75.4 +- 12.058 0.913 +- 0.004 0.88 +- 0.035
Chi-squared 0.01 573.3 +- 60.234 0.907 +- 0.003 0.907 +- 0.019
Chi-squared 0.05 812.9 +- 71.2 0.905 +- 0.003 0.891 +- 0.024
xtable(table_nonranking(nonranking_results, model, metrics),
       label = paste0(exp_prefix, "_nonranking_RF1000_table"),
       caption = paste0(experiment_name, " - averaged filtering results for random forest classifier (1000 trees)."))
% latex table generated in R 4.0.4 by xtable 1.8-4 package
% Sun Jun  6 21:59:21 2021
\begin{table}[ht]
\centering
\begin{tabular}{rlll}
  \hline
 & n-kmers selected & Accuracy & AUC \\ 
  \hline
FCBF & 9.8 +- 0.841 & 0.968 +- 0.006 & 0.971 +- 0.016 \\ 
  QuiPT 0.01 & 49.9 +- 9.273 & 0.912 +- 0.005 & 0.857 +- 0.037 \\ 
  QuiPT 0.05 & 75.4 +- 12.058 & 0.913 +- 0.004 & 0.88 +- 0.035 \\ 
  Chi-squared 0.01 & 573.3 +- 60.234 & 0.907 +- 0.003 & 0.907 +- 0.019 \\ 
  Chi-squared 0.05 & 812.9 +- 71.2 & 0.905 +- 0.003 & 0.891 +- 0.024 \\ 
   \hline
\end{tabular}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged filtering results for random forest classifier (1000 trees).} 
\label{exp1_1m_300s_50p_nonranking_RF1000_table}
\end{table}
plot_ranking_results(ranking_results, "AUC")

ggsave(paste0("plots/", exp_prefix, "_ranking_results_AUC.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_AUC.pdf}\n",
    "\\caption{", experiment_name, " - averaged AUC score for each ranking-based k-mer filtering technique.}\n",
    "\\label{fig:", exp_prefix,"_ranking_results_AUC}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC score for each ranking-based k-mer filtering technique.}
\label{fig:exp1_1m_300s_50p_ranking_results_AUC}
\end{figure}
plot_ranking_results(ranking_results, "Accuracy")

ggsave(paste0("plots/", exp_prefix, "_ranking_results_Accuracy.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_Accuracy.pdf}\n",
    "\\caption{", experiment_name, " - averaged accuracy for each ranking-based k-mer filtering technique.}\n",
    "\\label{fig:", exp_prefix,"_ranking_results_Accuracy}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each ranking-based k-mer filtering technique.}
\label{fig:exp1_1m_300s_50p_ranking_results_Accuracy}
\end{figure}
two_models_results <- lapply(ranking_results, function(x) x[(x$model %in% c("lm", "rf")) & (x$value %in% c(500, NA)), ])

plot_ranking_results(two_models_results, "AUC", ncol=1)

ggsave(paste0("plots/", exp_prefix, "_ranking_results_2models_AUC.pdf"))
Saving 12 x 6 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_2models_AUC.pdf}\n",
    "\\caption{", experiment_name, " - averaged AUC for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}\n",
    "\\label{fig:", exp_prefix,"_ranking_results_2models_AUC}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_2models_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}
\label{fig:exp1_1m_300s_50p_ranking_results_2models_AUC}
\end{figure}
two_models_results <- lapply(ranking_results, function(x) x[(x$model %in% c("lm", "rf")) & (x$value %in% c(500, NA)), ])

plot_ranking_results(two_models_results, "Accuracy", ncol=1)

ggsave(paste0("plots/", exp_prefix, "_ranking_results_2models_Accuracy.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_ranking_results_2models_Accuracy.pdf}\n",
    "\\caption{", experiment_name, " - averaged accuracy for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}\n",
    "\\label{fig:", exp_prefix,"_ranking_results_2models_Accuracy}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_ranking_results_2models_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each ranking-based k-mer filtering technique presented for regularized logistic regression and random forest classifiers.}
\label{fig:exp1_1m_300s_50p_ranking_results_2models_Accuracy}
\end{figure}
plot_ranking_results_w_nonranking(ranking_results, nonranking_results, "AUC", ncol=3)

ggsave(paste0("plots/", exp_prefix, "_results_AUC.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_AUC.pdf}\n",
    "\\caption{", experiment_name, " - averaged AUC score for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
    "\\label{fig:", exp_prefix,"_results_AUC.pdf}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC score for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_AUC.pdf}
\end{figure}
plot_ranking_results_w_nonranking(ranking_results, nonranking_results, "Accuracy", ncol=3)

ggsave(paste0("plots/", exp_prefix, "_results_Accuracy.pdf"))
Saving 12 x 10 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_Accuracy.pdf}\n",
    "\\caption{", experiment_name, " - averaged accuracy for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
    "\\label{fig:", exp_prefix,"_results_Accuracy.pdf}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each k-mer filtering method. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_Accuracy.pdf}
\end{figure}
two_models_results_nonranking <- lapply(nonranking_results,
                                        function(x) x[(x$model %in% c("lm", "rf")) & (x$value %in% c(500, NA)), ])

plot_ranking_results_w_nonranking(two_models_results, two_models_results_nonranking, "AUC", ncol=1)

ggsave(paste0("plots/", exp_prefix, "_results_2models_AUC.pdf"))
Saving 12 x 8 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_2models_AUC.pdf}\n",
    "\\caption{", experiment_name, " - averaged AUC score for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
    "\\label{fig:", exp_prefix,"_results_2models_AUC}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_2models_AUC.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged AUC score for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_2models_AUC}
\end{figure}
plot_ranking_results_w_nonranking(two_models_results, two_models_results_nonranking, "Accuracy", ncol=1)

ggsave(paste0("plots/", exp_prefix, "_results_2models_Accuracy.pdf"))
Saving 12 x 8 in image
cat(paste0("\\begin{figure}\n",
    "\\centering\n",
    "\\includegraphics[scale=0.52]{sections/plots/", exp_prefix,"_results_2models_Accuracy.pdf}\n",
    "\\caption{", experiment_name, " - averaged accuracy for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}\n",
    "\\label{fig:", exp_prefix,"_results_2models_Accuracy}\n",
    "\\end{figure}"))
\begin{figure}
\centering
\includegraphics[scale=0.52]{sections/plots/exp1_1m_300s_50p_results_2models_Accuracy.pdf}
\caption{Experiment 1 (1 motif, 300 sequences, 50\% positive sequences) - averaged accuracy for each k-mer filtering method presented for regularized logistic regression and random forest classifiers. For nonranking methods, number of selected k-mers is averaged over all experiment iterations.}
\label{fig:exp1_1m_300s_50p_results_2models_Accuracy}
\end{figure}